Methodology of above: - only had population for total residential (includes mutli family, etc) - but had SF data for single family homes. got percentage that single family homes make up of total residential, to multiply by the usage per capita for total residential to get usage per capita for single family homes - helps to control for neighborhoods that might have different proportions of homes vs. multifamily complexes, mobile homes, etc MAP: ECPH.

## 
## Call:
## lm(formula = energy_intensity ~ weighted_avg_income, data = income_engint_2013)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -44713  -3855   1088   5553  47175 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          5.947e+04  9.254e+02   64.27   <2e-16 ***
## weighted_avg_income -1.693e-01  8.882e-03  -19.06   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10590 on 1382 degrees of freedom
## Multiple R-squared:  0.2081, Adjusted R-squared:  0.2075 
## F-statistic: 363.2 on 1 and 1382 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = usage_percap ~ weighted_avg_income, data = income_ECPC_2013)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -21559589  -4207766    227923   4259318  58316276 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -4.801e+05  6.309e+05  -0.761    0.447    
## weighted_avg_income  1.833e+02  6.052e+00  30.292   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7182000 on 1364 degrees of freedom
## Multiple R-squared:  0.4022, Adjusted R-squared:  0.4017 
## F-statistic: 917.6 on 1 and 1364 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = ecph ~ owned_units_perc, data = ECPH_vs_TENURE_2013)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -39261390  -7242051  -1431296   4866181 202095189 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      28578202    1101564   25.94   <2e-16 ***
## owned_units_perc 56948772    1733185   32.86   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13850000 on 1367 degrees of freedom
## Multiple R-squared:  0.4413, Adjusted R-squared:  0.4409 
## F-statistic:  1080 on 1 and 1367 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = ecpc ~ owned_occ_perc, data = ECPC_vs_TENURE_2013)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -20360763  -3875489   -271107   2890330  64527492 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -1996103     517724  -3.856 0.000121 ***
## owned_occ_perc 32557036     808282  40.279  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6278000 on 1364 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.5433, Adjusted R-squared:  0.5429 
## F-statistic:  1622 on 1 and 1364 DF,  p-value: < 2.2e-16

The above (2) plots show that increased ownership is associated with increased consumption, both when measured per household and per occupant

## 
## Call:
## lm(formula = energy_intensity ~ owned_units_perc, data = EISF_vs_TENURE_2013)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -44493  -4502   1289   5325  54132 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       51485.6      914.1   56.33   <2e-16 ***
## owned_units_perc -14675.9     1436.1  -10.22   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11480 on 1382 degrees of freedom
## Multiple R-squared:  0.07026,    Adjusted R-squared:  0.06959 
## F-statistic: 104.4 on 1 and 1382 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = ecph ~ weighted_avg_value, data = ECPH_vs_VALUE_2013)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -50599004  -9983583   -115997   8491055 199325888 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        4.219e+07  1.135e+06   37.16   <2e-16 ***
## weighted_avg_value 3.463e+01  1.771e+00   19.56   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16360000 on 1366 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2188, Adjusted R-squared:  0.2182 
## F-statistic: 382.6 on 1 and 1366 DF,  p-value: < 2.2e-16

## 
## Call:
## lm(formula = ecph ~ weighted_avg_years_in_home, data = ECPH_vs_YEARS_in_home_2013)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -37873158  -8410277  -2560795   4795491 204376242 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 9797196    2399111   4.084 4.69e-05 ***
## weighted_avg_years_in_home  2568290     114773  22.377  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15850000 on 1367 degrees of freedom
## Multiple R-squared:  0.2681, Adjusted R-squared:  0.2676 
## F-statistic: 500.7 on 1 and 1367 DF,  p-value: < 2.2e-16

Equity Analysis

Equity analysis in this case is tricky, since consumption data from the utility (PG&E) was, for privacy reasons, aggregated to the census tract level as the smallest geographical granularity. This means that we can compare a few census tracts by their overall efficiency scores (EISF for single family homes) and their overall racial makeup, but we cannot say for certain which specific houses operate more or less efficiently and who exactly lives in those houses. So, the tracts with both the highest and the lowest EISF scores (shown in the EISF maps) will be inspected for their racial make up to see if any trends are apparent.

As we can see above..

Scrap Code Below: